1. Field of the Invention
The present invention relates to a method, system, and computer program for analyzing, organizing and ordering items within a list based on a user's profile, friends' preferences or user's mood. More specifically, the method, system, and computer program provide a personalized list of items (movies, songs, books, or other content) for the user based on specific characteristics of that user.
2. Discussion of the Background
Consumers receive information about various products (for example movies, songs, books, games, stock, insurance, interest rates, etc.) from multiple sources and that information has become so vast with today's capabilities of receiving information (email, instant messaging, mobile phone, internet, content on demand, cable TV, etc.) that a consumer is not able to browse all that information to make an informed decision about purchasing the desired products. More specifically, a user is faced today, for example, with the task of selecting a movie from a database of movies that includes over 5,000 artists, 2,000 directors, and over 100 genres. That task is almost impossible especially when the user has to take into account the preferred artist, director, and genre. In addition to this vast array of information, the mood of the consumer and the circle of friends of the consumer also contribute to the making of the informed decision about buying the products.
However, there is no content service available that takes into consideration specific criteria used by a particular consumer/user when purchasing a product. None of the existing content services integrate all those criteria and recommend the products of a certain class (for example movies) to the consumer such that the products are ranked according to the importance to the consumer based on the above noted criteria (all the information available on the market, the preferences of the circle of friends of the user, and the mood of the user).
Recommendation services or content on demand services recommend items (usually movies and/or songs) based on a database of know preferences of a majority of consumers. Thus, these services ignore the specifics of a particular consumer and its mood. In other words, the available services create an average consumer profile and recommend to all users the same products irrespective of the user's particulars.
As the digital TV infrastructure is developing worldwide, the content on demand offer is increasing. For example, in the United States, Comcast has over 2,000 hours of content available on demand for premium customers. It is estimated that by 2008 the content services as MovieLink, CinemaNow, Music Match, Soapcity, etc, would have over 20,000 movies and over 3 million music tracks available for on-demand applications. Also, because Set Top Boxes are increasingly becoming intelligent, it is estimated that by 2008 more than 40 million users only in the United States will have advanced Set Top Boxes in their home and therefore, able to navigate and select media content from the available content on demand services.
However, the content on demand available services are confronted with the vast information discussed above, the lack of user specific information, and the failure to provide content tailored for a specific user. The conventional technique commonly used by the content on demand services for selecting and recommending movies are identifying items which are similar to items that are known to be of interest to the majority of the users. For example, based on surveys that include the opinions of thousands of users, a common pattern (majority of users like action movies) is determined and based on that common pattern the content on demand service offers to the users across the board mainly action movies.
More sophisticated services, for example a content-based website service may operate by analyzing the user's favorite web pages to generate a profile of commonly-occurring terms and then use this profile to search for other web pages that include some or all of these terms. This method takes into account some preferences of the user but those preferences are limited because the method does not differentiate, for example, a movie directly selected by the consumer by its title or by its leading artist. In other words, the above described method does not categorize the artists preferred by the user and does not distinguish between a 3rd preferred artist and a 4th preferred artist when recommending a movie to the user.
Existing content-based recommendation systems have other significant limitations. For example, content-based methods generally do not provide any mechanism for evaluating the quality of popularity of a product. In addition, content-based methods generally require that products/items that are searched by searching engines include some form of content that is amenable to features extraction. Thus, content-based systems tend to be poorly suited for recommending movies, music titles, etc. or other items that have little or no useful content.
Another system that is capable of producing some content recommendations is the Interactive Program Guide (IPG), which is becoming the entertainment portal for TV viewers. In-stat/MDR, a leading provider of actionable research, assessments and market forecasts of semiconductor and advanced communications equipment and services, expects the worldwide IPG market value to grow to nearly $1 Billion by 2008. IPGs will help end-users to find a TV program, movie, or sporting event from among listings of thousands of available options, and then make it easy for the users to select the program for viewing, tag it with a reminder for later, or even set up a recording to capture the show for time-shifting on a Personal Video Recorder.
However, the problem of the existing content on demand and IPG systems is that the content to be organized and presented to the user is vast and very diversified such that various categories are not related to each other. Thus, the existing systems take into consideration only those categories that can be related to each other and not all the categories available. For example, known systems take into the consideration the purchase history of the user regarding certain items or certain reviewers' recommendations about those items but not the preferences of the user's friends and the mood of the user.
Further, none of today's content navigation systems takes into consideration multiple factors that can influence the user's choice about content, as for example, in the case that the user is looking for a movie, the artist, director, basic description, rating, box office results, genre, band, producer and other factors that can play a role in influencing the user's choice about the movie. In other words, none of today's content navigation systems cater to such a low level breakdown of content to perform personalization for the individual user.
Furthermore, none of today's content navigation system takes into consideration the opinions of friends, colleagues, family members, or related persons to the user regarding the user's choice of content. Thus, the social effect on the user when deciding about the content to be purchased is not a factor considered by the available content navigation systems.
Another problem of the available content navigation systems is that the mood of the user just prior to receiving or selecting the item is not known and not taken into consideration. Therefore, even if the user likes a certain item, the mood of the user before purchasing that item can be such that is not compatible with that item. For example, if the user typically likes dramas, other conditions, e.g., a promotion at work or a death in the family, may make it inappropriate to suggest a drama at the current time. Because the available content navigation systems are not capable of factoring in the user's mood, the system would still recommend a drama movie to the user. Therefore, the conventional content navigation systems do not account for the existing mood of the user at the time of purchasing.
The conceptual model of the background art content systems discussed above is schematically illustrated in FIG. 1. FIG. 1 shows a user 110 that interacts with the content on demand system 120. The content on demand system 120 offers the user 110 a list of movies and the user 110 selects a preferred movie from the list based on (i) information available in the media about the movie (called meta-content) 130 that the user has read, and (ii) the information about the movies received from his friends 140. Thus, the user 110 “makes up his mind” based on the information (i) and (ii) and selects a desired movie from the list provided by the content on demand 120. Alternatively, the user 110 physically visits the store/provider 150 and purchase/rents the movie based on the above information.
It has been shown that Internet-based businesses like Amazon, DVD rental portal NetFlix, and TiVo have a revenue increase between 30% and 60% by a simple user assistance/recommendation engine as described above and which suggests certain content to the users. Traditionally, 80% of the entertainment industry's revenue comes via 20% of the content they produce. In a typical revenue share model, a content owner takes between 30 and 60% of the revenue and the remaining amount goes to the service provider. The more popular the content, the larger the content owner's revenue share. Thus, a content navigation and recommendation system that better suits each user will help the users increase the consumption of lesser known content (for the content that matches the criteria of the user).